rPAC: Route based pathway analysis for cohorts of gene expression data sets.

Pathway analysis is a popular method aiming to derive biological interpretation from high-throughput gene expression studies. However, existing methods focus mostly on identifying which pathway or pathways could have been perturbed, given differential gene expression patterns. In this paper, we present a novel pathway analysis framework, namely rPAC, which decomposes each signaling pathway route into two parts, the upstream portion of a transcription factor (TF) block and the downstream portion from the TF block and generates a pathway route perturbation analysis scheme examining disturbance scores assigned to both parts together. This rPAC scoring is further applied to a cohort of gene expression data sets which produces two summary metrics, "Proportion of Significance" (PS) and "Average Route Score" (ARS), as quantitative measures discerning perturbed pathway routes within and/or between cohorts. To demonstrate rPAC's scoring competency, we first used a large amount of simulated data and compared the method's performance against those by conventional methods in terms of power curve. Next, we performed a case study involving three epithelial cancer data sets from The Cancer Genome Atlas (TCGA). The rPAC method revealed specific pathway routes as potential cancer type signatures. A deeper pathway analysis of sub-groups (i.e., age groups in COAD or cancer sub-types in BRCA) resulted in pathway routes that are known to be associated with the sub-groups. In addition, multiple previously uncharacterized pathways routes were identified, potentially suggesting that rPAC is better in deciphering etiology of a disease than conventional methods particularly in isolating routes and sections of perturbed pathways in a finer granularity.

Single-cell Transcriptome Landscape of DNA Methylome Regulators Associated with Orofacial Clefts in the Mouse Dental Pulp.

OBJECTIVE: Significant evidence links epigenetic processes governing the dynamics of DNA methylation and demethylation to an increased risk of syndromic and nonsyndromic cleft lip and/or cleft palate (CL/P). Previously, we characterized mesenchymal stem/stromal cells (MSCs) at different stages of osteogenic differentiation in the mouse incisor dental pulp. The main objective of this research was to characterize the transcriptional landscape of regulatory genes associated with DNA methylation and demethylation at a single-cell resolution. DESIGN: We used single-cell RNA sequencing (scRNA-seq) data to characterize transcriptome in individual subpopulations of MSCs in the mouse incisor dental pulp. SETTINGS: The biomedical research institution. PATIENTS/PARTICIPANTS: This study did not include patients. INTERVENTIONS: This study collected and analyzed data on the single-cell RNA expssion in the mouse incisor dental pulp. MAIN OUTCOME MEASURE(S): Molecular regulators of DNA methylation/demethylation exhibit differential transcriptional landscape in different subpopulations of osteogenic progenitor cells. RESULTS: scRNA-seq analysis revealed that genes encoding DNA methylation and demethylation enzymes (DNA methyltransferases and members of the ten-eleven translocation family of methylcytosine dioxygenases), methyl-DNA binding domain proteins, as well as transcription factors and chromatin remodeling proteins that cooperate with DNA methylation machinery are differentially expressed within distinct subpopulations of MSCs that undergo different stages of osteogenic differentiation. CONCLUSIONS: These findings suggest some mechanistic insights into a potential link between epigenetic alterations and multifactorial causes of CL/P phenotypes.

Predicting the targets of IRF8 and NFATc1 during osteoclast differentiation using the machine learning method framework cTAP.

BACKGROUND: Interferon regulatory factor-8 (IRF8) and nuclear factor-activated T cells c1 (NFATc1) are two transcription factors that have an important role in osteoclast differentiation. Thanks to ChIP-seq technology, scientists can now estimate potential genome-wide target genes of IRF8 and NFATc1. However, finding target genes that are consistently up-regulated or down-regulated across different studies is hard because it requires analysis of a large number of high-throughput expression studies from a comparable context. METHOD: We have developed a machine learning based method, called, Cohort-based TF target prediction system (cTAP) to overcome this problem. This method assumes that the pathway involving the transcription factors of interest is featured with multiple "functional groups" of marker genes pertaining to the concerned biological process. It uses two notions, Gene-Present Sufficiently (GP) and Gene-Absent Insufficiently (GA), in addition to log2 fold changes of differentially expressed genes for the prediction. Target prediction is made by applying multiple machine-learning models, which learn the patterns of GP and GA from log2 fold changes and four types of Z scores from the normalized cohort's gene expression data. The learned patterns are then associated with the putative transcription factor targets to identify genes that consistently exhibit Up/Down gene regulation patterns within the cohort. We applied this method to 11 publicly available GEO data sets related to osteoclastgenesis. RESULT: Our experiment identified a small number of Up/Down IRF8 and NFATc1 target genes as relevant to osteoclast differentiation. The machine learning models using GP and GA produced NFATc1 and IRF8 target genes different than simply using a log2 fold change alone. Our literature survey revealed that all predicted target genes have known roles in bone remodeling, specifically related to the immune system and osteoclast formation and functions, suggesting confidence and validity in our method. CONCLUSION: cTAP was motivated by recognizing that biologists tend to use Z score values present in data sets for the analysis. However, using cTAP effectively presupposes assembling a sizable cohort of gene expression data sets within a comparable context. As public gene expression data repositories grow, the need to use cohort-based analysis method like cTAP will become increasingly important.

Excitotoxic stimulation activates distinct pathogenic and protective expression signatures in the hippocampus.

Excitotoxic events underlying ischaemic and traumatic brain injuries activate degenerative and protective pathways, particularly in the hippocampus. To understand opposing pathways that determine the brain's response to excitotoxicity, we used hippocampal explants, thereby eliminating systemic variables during a precise protocol of excitatory stimulation. N-methyl-d-aspartate (NMDA) was applied for 20 min and total RNA isolated one and 24 h later for neurobiology-specific microarrays. Distinct groups of genes exhibited early vs. delayed induction, with 63 genes exclusively reduced 24-h post-insult. Egr-1 and NOR-1 displayed biphasic transcriptional modulation: early induction followed by delayed suppression. Opposing events of NMDA-induced genes linked to pathogenesis and cell survival constituted the early expression signature. Delayed degenerative indicators (up-regulated pathogenic genes, down-regulated pro-survival genes) and opposing compensatory responses (down-regulated pathogenic genes, up-regulated pro-survival genes) generated networks with temporal gene profiles mirroring coexpression network clustering. We then used the expression profiles to test whether NF-κB, a potent transcription factor implicated in both degenerative and protective pathways, is involved in the opposing responses. The NF-κB inhibitor MG-132 indeed altered NMDA-mediated transcriptional changes, revealing components of opposing expression signatures that converge on the single response element. Overall, this study identified counteracting avenues among the distinct responses to excitotoxicity, thereby suggesting multi-target treatment strategies and implications for predictive medicine.

A framework using topological pathways for deeper analysis of transcriptome data.

BACKGROUND: Pathway analysis is one of the later stage data analysis steps essential in interpreting high-throughput gene expression data. We propose a set of algorithms which given gene expression data can recognize which portion of sub-pathways are actively utilized in the biological system being studied. The degree of activation is measured by conditional probability of the input expression data based on the Bayesian Network model constructed from the topological pathway. RESULTS: We demonstrate the effectiveness of our pathway analysis method by conducting two case studies. The first one applies our method to a well-studied temporal microarray data set for the cell cycle using the KEGG Cell Cycle pathway. Our method closely reproduces the biological claims associated with the data sets, but unlike the original work ours can produce how pathway routes interact with each other above and beyond merely identifying which pathway routes are involved in the process. The second study applies the method to the p53 mutation microarray data to perform a comparative study. CONCLUSIONS: We show that our method achieves comparable performance against all other pathway analysis systems included in this study in identifying p53 altered pathways. Our method could pave a new way of carrying out next generation pathway analysis.

TriPOINT: a software tool to prioritize important genes in pathways and their non-coding regulators.

SUMMARY: Current approaches for pathway analyses focus on representing gene expression levels on graph representations of pathways and conducting pathway enrichment among differentially expressed genes. However, gene expression levels by themselves do not reflect the overall picture as non-coding factors play an important role to regulate gene expression. To incorporate these non-coding factors into pathway analyses and to systematically prioritize genes in a pathway we introduce a new software: Triangulation of Perturbation Origins and Identification of Non-Coding Targets. Triangulation of Perturbation Origins and Identification of Non-Coding Targets is a pathway analysis tool, implemented in Java that identifies the significance of a gene under a condition (e.g. a disease phenotype) by studying graph representations of pathways, analyzing upstream and downstream gene interactions and integrating non-coding regions that may be regulating gene expression levels. AVAILABILITY AND IMPLEMENTATION: The TriPOINT open source software is freely available at https://github.uconn.edu/ajt06004/TriPOINT under the GPL v3.0 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A route-based pathway analysis framework integrating mutation information and gene expression data.

We propose a new way of analyzing biological pathways in which the analysis combines both transcriptome data and mutation information and uses the outcome to identify "routes" of aberrant pathways potentially responsible for the etiology of disease. Each pathway route is encoded as a Bayesian Network which is initialized with a sequence of conditional probabilities which are designed to encode directionality of regulatory relationships encoded in the pathways, i.e. activation and inhibition relationships. First, we demonstrate the effectiveness of our model through simulation in which the model was able to easily separate Test samples from Control samples using fictitiously perturbed pathway routes. Second, we apply our model to analyze the Breast Cancer data set, available from TCGA, against many cancer pathways available from KEGG and rank the significance of identified pathways. The outcome is consistent with what have already been reported in the literature. Third, survival analysis has been carried out on the same data set by using pathway routes as features. Overall, we envision that our model of using pathway routes for analysis can further refine the conventional ways of subtyping cancer patients as it can discover additional characteristics specific to individual's tumor.

Screening Gene Knockout Mice for Variation in Bone Mass: Analysis by µCT and Histomorphometry.

PURPOSE OF REVIEW: The international mouse phenotyping consortium (IMPC) is producing defined gene knockout mouse lines. Here, a phenotyping program is presented that is based on micro-computed tomography (µCT) assessment of distal femur and vertebra. Lines with significant variation undergo a computer-based bone histomorphometric analysis. RECENT FINDINGS: Of the 220 lines examined to date, approximately 15% have a significant variation (high or low) by µCT, most of which are not identified by the IMPC screen. Significant dimorphism between the sexes and bone compartments adds to the complexity of the skeletal findings. The µCT information that is posted at www.bonebase.org can group KOMP lines with similar morphological features. The histological data is presented in a graphic form that associates the cellular features with a specific anatomic group. The web portal presents a bone-centric view appropriate for the skeletal biologist/clinician to organize and understand the large number of genes that can influence skeletal health. Cataloging the relative severity of each variant is the first step towards compiling the dataset necessary to appreciate the full polygenic basis of degenerative bone disease.

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

UNLABELLED: Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. AVAILABILITY: QuIN's web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

A Novel Epi-drug Therapy Based on the Suppression of BET Family Epigenetic Readers.

Recent progress in epigenetic research has made a profound influence on pharmacoepigenomics, one of the fastest growing disciplines promising to provide new epi-drugs for the treatment of a broad range of diseases. Histone acetylation is among the most essential chromatin modifications underlying the dynamics of transcriptional activation. The acetylated genomic regions recruit the BET (bromodomain and extra-terminal) family of bromodomains (BRDs), thereby serving as a molecular scaffold in establishing RNA polymerase II specificity. Over the past several years, the BET epigenetic readers have become the main targets for drug therapy. The discovery of selective small-molecule compounds with capacity to inhibit BET proteins has paved a path for developing novel strategies against cancer, cardiovascular, skeletal, and inflammatory diseases. Therefore, further research into small chemicals impeding the regulatory activity of BRDs could offer therapeutic benefits for many health problems including tumor growth, heart disease, oral, and bone disorders.

The LG/J murine strain exhibits near-normal tendon biomechanical properties following a full-length central patellar tendon defect.

PURPOSE OF THE STUDY: Identifying biological success criteria is needed to improve therapies, and one strategy for identifying them is to analyze the RNA transcriptome for successful and unsuccessful models of tendon healing. We have characterized the MRL/MpJ murine strain and found improved mechanical outcomes following a central patellar tendon (PT) injury. In this study, we evaluate the healing of the LG/J murine strain, which comprises 75% of the MRL/MpJ background, to determine if the LG/J also exhibits improved biomechanical properties following injury and to determine differentially expressed transcription factors across the C57BL/6, MRL/MpJ and the LG/J strains during the early stages of healing. MATERIALS AND METHODS: A full-length, central PT defect was created in 16-20 week old MRL/MpJ, LG/J, and C57BL/6 murine strains. Mechanical properties were assessed at 2, 5, and 8 weeks post surgery. Transcriptomic expression was assessed at 3, 7, and 14 days following injury using a novel clustering software program to evaluate differential expression of transcription factors. RESULTS: Average LG/J structural properties improved to 96.7% and 97.2% of native LG/J PT stiffness and ultimate load by 8 weeks post surgery, respectively. We found the LG/J responded by increasing expression of transcription factors implicated in the inflammatory response and collagen fibril organization. CONCLUSIONS: The LG/J strain returns to normal structural properties by 8 weeks, with steadily increasing properties at each time point. Future work will characterize the cell populations responding to injury and investigate the role of the differentially expressed transcription factors during healing.

GABRA2 Alcohol Dependence Risk Allele is Associated with Reduced Expression of Chromosome 4p12 GABAA Subunit Genes in Human Neural Cultures.

BACKGROUND: Genetic variation in a region of chromosome 4p12 that includes the GABAA subunit gene GABRA2 has been reproducibly associated with alcohol dependence (AD). However, the molecular mechanisms underlying the association are unknown. This study examined correlates of in vitro gene expression of the AD-associated GABRA2 rs279858*C-allele in human neural cells using an induced pluripotent stem cell (iPSC) model system. METHODS: We examined mRNA expression of chromosome 4p12 GABAA subunit genes (GABRG1, GABRA2, GABRA4, and GABRB1) in 36 human neural cell lines differentiated from iPSCs using quantitative polymerase chain reaction and next-generation RNA sequencing. mRNA expression in adult human brain was examined using the BrainCloud and BRAINEAC data sets. RESULTS: We found significantly lower levels of GABRA2 mRNA in neural cell cultures derived from rs279858*C-allele carriers. Levels of GABRA2 RNA were correlated with those of the other 3 chromosome 4p12 GABAA genes, but not other neural genes. Cluster analysis based on the relative RNA levels of the 4 chromosome 4p12 GABAA genes identified 2 distinct clusters of cell lines, a low-expression cluster associated with rs279858*C-allele carriers and a high-expression cluster enriched for the rs279858*T/T genotype. In contrast, there was no association of genotype with chromosome 4p12 GABAA gene expression in postmortem adult cortex in either the BrainCloud or BRAINEAC data sets. CONCLUSIONS: AD-associated variation in GABRA2 is associated with differential expression of the entire cluster of GABAA subunit genes on chromosome 4p12 in human iPSC-derived neural cell cultures. The absence of a parallel effect in postmortem human adult brain samples suggests that AD-associated genotype effects on GABAA expression, although not present in mature cortex, could have effects on regulation of the chromosome 4p12 GABAA cluster during neural development.

Genome-wide analysis of FoxO1 binding in hepatic chromatin: potential involvement of FoxO1 in linking retinoid signaling to hepatic gluconeogenesis.

The forkhead transcription factor FoxO1 is a critical regulator of hepatic glucose and lipid metabolism, and dysregulation of FoxO1 function has been implicated in diabetes and insulin resistance. We globally identified FoxO1 occupancy in mouse hepatic chromatin on a genome-wide level by chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq). To establish the specific functional significance of FoxO1 against other FoxO proteins, ChIP-seq was performed with chromatin from liver-specific FoxO1 knockout and wild-type mice. Here we identified 401 genome-wide FoxO1-binding locations. Motif search reveals a sequence element, 5' GTAAACA 3', consistent with a previously known FoxO1-binding site. Gene set enrichment analysis shows that the data from FoxO1 ChIP-seq are highly correlated with the global expression profiling of genes regulated by FoxO1, demonstrating the functional relevance of our FoxO1 ChIP-seq study. Interestingly, gene ontology analysis reveals the functional significance of FoxO1 in retinoid metabolic processes. We show here that FoxO1 directly binds to the genomic sites for the genes in retinoid metabolism. Notably, deletion of FoxO1 caused a significantly reduced induction of Pck1 and Pdk4 in response to retinoids. As Pck1 and Pdk4 are downstream targets of retinoid signaling, these results suggest that FoxO1 plays a potential role in linking retinoid metabolism to hepatic gluconeogenesis.

vSPACE: Exploring Virtual Spatial Representation of Articular Chondrocytes at the Single-Cell Level.

Single cell RNA sequencing technology has been dramatically changing how gene expression studies are performed. However, its use has been limited to identifying subtypes of cells by comparing cells' gene expression levels in an unbiased manner to produce a 2D plot (e.g., UMAP/tSNE). We developed a new method of placing cells in 2D space. This system, called vSPACE, shows a virtual spatial representation of scRNAseq data obtained from human articular cartilage by emulating the concept of spatial transcriptomics technology, but virtually. This virtual 2D plot presentation of human articular cartage cells generates several zonal distribution patterns, in one or multiple genes at a time, reveling patterns that scientists can appreciate as imputed spatial distribution patterns along the zonal axis. The discovered patterns are explainable and remarkably consistent across all six healthy doners despite their respectively different clinical variables (age and sex), suggesting the confidence of the discovered patterns.

CGCom: a framework for inferring Cell-cell Communication based on Graph Neural Network.

Cell-cell communication is crucial in maintaining cellular homeostasis, cell survival and various regulatory relationships among interacting cells. Thanks to recent advances of spatial transcriptomics technologies, we can now explore if and how cells' proximal information available from spatial transcriptomics datasets can be used to infer cell-cell communication. Here we present a cell-cell communication inference framework, called CGCom, which uses a graph neural network (GNN) to learn communication patterns among interacting cells by combining single-cell spatial transcriptomic datasets with publicly available ligand-receptor information and the molecular regulatory information down-stream of the ligand-receptor signaling. To evaluate the performance of CGCom, we applied it to mouse embryo seqFISH datasets. Our results demonstrate that CGCom can not only accurately infer cell communication between individual cell pairs but also generalize its learning to predict communication between different cell types. We compared the performance of CGCom with two existing methods, CellChat and CellPhoneDB, and our comparative study revealed both common and unique communication patterns from the three approaches. Commonly found communication patterns include three sets of ligand-receptor communication relationships, one between surface ectoderm cells and spinal cord cells, one between gut tube cells and endothelium, and one between neural crest and endothelium, all of which have already been reported in the literature thus offering credibility of all three methods. However, we hypothesize that CGCom is superior in reducing false positives thanks to its use of cell proximal information and its learning between specific cell pairs rather than between cell types. CGCom is a GNN-based solution that can take advantage of spatially resolved single-cell transcriptomic data in predicting cell-cell communication with a higher accuracy.

Effectiveness and Safety of Progressive Loading-Motion Style Acupuncture Treatment for Acute Low Back Pain after Traffic Accidents: A Randomized Controlled Trial.

BACKGROUND: Traffic injuries include acute low back pain (LBP) needing active treatment to prevent chronicity. This two-armed, parallel, assessor-blinded, randomized controlled trial evaluated the effectiveness and safety of progressive loading-motion style acupuncture treatment (PL-MSAT) for acute LBP following traffic accidents. METHODS: Based on an effect size of 1.03, 104 participants were recruited and divided in a 1:1 ratio into PL-MAST and control groups using block randomization. Both groups underwent integrative Korean medicine treatment (IKMT) daily; only the PL-MSAT group underwent three PL-MSAT sessions. The outcomes were assessed before and after the treatment sessions and at 1 and 3 months post-discharge. The primary outcome was the difference in the numeric rating scale (NRS) for LBP. The secondary outcomes included a visual analog scale for LBP, leg pain status, the Oswestry disability index, lumbar active range of motion (ROM), quality of life, Patient Global Impression of Change, and Post-Traumatic Stress Disorder Checklist adverse events. RESULTS: In the modified intention-to-treat analysis, 50 and 51 participants were included in the PL-MSAT and control groups. On Day 4, the mean LBP NRS score was 3.67 (3.44-3.90) in the PL-MSAT group, indicating a significantly lower NRS 0.77 (0.44-1.11) compared to 4.44 (4.20-4.68) for the control group (p < 0.001). The PL-MSAT group exhibited greater ROM flexion (-5.31; -8.15 to -2.48) and extension (-2.09; -3.39 to -0.80). No significant differences were found for the secondary outcomes and follow-ups. CONCLUSIONS: Compared with IKMT alone, PL-MSAT plus IKMT showed significantly better outcomes for reducing pain and increasing the ROM in acute LBP.

The chromatin accessibility landscape in the dental pulp of mouse molars and incisors.

The dental pulp is a promising source of progenitor cells for regenerative medicine. The natural function of dental pulp is to produce odontoblasts to generate reparative dentin. Stem cells within the pulp tissue originate from the migrating neural crest cells and possess mesenchymal stem cell properties with the ability to differentiate into multiple lineages. To elucidate the transcriptional control mechanisms underlying cell fate determination, we compared the transcriptome and chromatin accessibility in primary dental pulp tissue derived from 5-6-day-old mice. Using RNA sequencing and assay for transposase-accessible chromatin using sequencing (ATAC-seq), we correlated gene expression with chromatin accessibility. We found that the majority of ATAC-seq peaks were concentrated at genes associated with development and cell differentiation. Most of these genes were highly expressed in the mouse dental pulp. Surprisingly, we uncovered a group of genes encoding master transcription factors that were not expressed in the dental pulp but retained open chromatin states. Within this group, we identified key developmental genes important for specification of the neural crest, adipocyte, neural, myoblast, osteoblast and hepatocyte lineages. Collectively, our results uncover a complex relationship between gene expression and the chromatin accessibility landscape in the mouse dental pulp.

Genome-wide distribution of 5hmC in the dental pulp of mouse molars and incisors.

The dental pulp is critical for the production of odontoblasts to create reparative dentin. In recent years, dental pulp has become a promising source of mesenchymal stem cells that are capable of differentiating into multiple cell types. To elucidate the transcriptional control mechanisms specifying the early phases of odontoblast differentiation, we analysed the DNA demethylation pattern associated with 5-hydroxymethylcytosine (5hmC) in the primary murine dental pulp. 5hmC plays an important role in chromatin accessibility and transcriptional control by modelling a dynamic equilibrium between DNA methylation and demethylation. Our research revealed 5hmC enrichment along genes and non-coding regulatory regions associated with specific developmental pathways in the genome of mouse incisor and molar dental pulp. Although the overall distribution of 5hmC is similar, the intensity and location of the 5hmC peaks significantly differs between the incisor and molar pulp genome, indicating cell type-specific epigenetic variations. Our study suggests that the differential DNA demethylation pattern could account for the distinct regulatory mechanisms underlying the tooth-specific ontogenetic programs.

Single-Cell Transcriptome Analysis Defines Expression of Kabuki Syndrome-Associated KMT2D Targets and Interacting Partners.

Objectives. Kabuki syndrome (KS) is a rare genetic disorder characterized by developmental delay, retarded growth, and cardiac, gastrointestinal, neurocognitive, renal, craniofacial, dental, and skeletal defects. KS is caused by mutations in the genes encoding histone H3 lysine 4 methyltransferase (KMT2D) and histone H3 lysine 27 demethylase (KDM6A), which are core components of the complex of proteins associated with histone H3 lysine 4 methyltransferase SET1 (SET1/COMPASS). Using single-cell RNA data, we examined the expression profiles of Kmt2d and Kdm6a in the mouse dental pulp. In the incisor pulp, Kmt2d and Kdm6a colocalize with other genes of the SET1/COMPASS complex comprised of the WD-repeat protein 5 gene (Wdr5), the retinoblastoma-binding protein 5 gene (Rbbp5), absent, small, and homeotic 2-like protein-encoding gene (Ash2l), nuclear receptor cofactor 6 gene (Ncoa6), and Pax-interacting protein 1 gene (Ptip1). In addition, we found that Kmt2d and Kdm6a coexpress with the downstream target genes of the Wingless and Integrated (WNT) and sonic hedgehog signaling pathways in mesenchymal stem/stromal cells (MSCs) at different stages of osteogenic differentiation. Taken together, our results suggest an essential role of KMT2D and KDK6A in directing lineage-specific gene expression during differentiation of MSCs.

Single-cell transcriptomics defines Dot1L interacting partners and downstream target genes in the mouse molar dental pulp.

Although histone methyltransferases are implicated in many key developmental processes, the contribution of individual chromatin modifiers in dental tissues is not well understood. Using single-cell RNA sequencing, we examined the expression profiles of the disruptor of telomeric silencing 1-like (Dot1L) gene in the postnatal day 5 mouse molar dental pulp. Dot1L is the only known enzyme that methylates histone 3 on lysine 79, a modification associated with gene expression. Our research revealed 15 distinct clusters representing different populations of mesenchymal stromal cells (MSCs), immune cells, pericytes, ameloblasts and endothelial cells. We documented heterogeneity in gene expression across different subpopulations of MSCs, a good indicator that these stromal progenitors undergo different phases of osteogenic differentiation. Interestingly, although Dot1L was broadly expressed across all cell clusters within the molar dental pulp, our analyses indicated specific enrichment of Dot1L within two clusters of MSCs, as well as cell clusters characterized as ameloblasts and endothelial cells. Moreover, we detected Dot1L co-expression with protein interactors involved in epigenetic activation such as Setd2, Sirt1, Brd4, Isw1, Bptf and Suv39h1. In addition, Dot1L was co-expressed with Eed2, Cbx3 and Dnmt1, which encode epigenetic factors associated with gene silencing and heterochromatin formation. Dot1l was co-expressed with downstream targets of the insulin growth factor and WNT signaling pathways, as well as genes involved in cell cycle progression. Collectively, our results suggest that Dot1L may play key roles in orchestrating lineage-specific gene expression during MSC differentiation.